外文翻译毕业设计题目：指纹识别与研究原文：Fingenjidnt Identification andVerification System usingMinutiae Matching译文：指纹识别和验证的匹配系统原文：Fingerprint Identification and Verification System using MinutiaeMatchingAbstract: Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) global ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure.This paper presents the implementation of a minutiae based approach to fingerprint identification and verification and serves as a review of the different techniques used in various steps in the development of minutiae based Automatic Fingerprint Identification Systems (AFIS). The technique conferred in this paper is based on the extraction of minutiae from the thinned, binarized and segmented version of a fingerprint image. The system uses fingerprint classification for indexing during fingerprint matching which greatly enhances the performance of the matching algorithm. Good results (-92% accuracy) were obtained using the FVC2000 fingerprint databases・1. INTRODUCTIONFingerprints have been in use for biometric recog nition since long because of their high acceptability, immutability and individuality. Immutability refers to the persistence of the fingerprints over time whereas individuality is related to the uniqueness of ridge details across individuals. The probability that two fingerprints arc alike is 1 in 1.9 x These featuresmake the use of fingerprints extremely effective in areas where the provision of a high degree of security is an issue. The major steps involved in automated fingerpri nt recog nition include a) Fingerprint Acquisition, b) Fingerprint Segmentation, c) Fingerprint Image Enhancement, d) Feature Extraction c) Minutiae Matching, f) Fingerprint Classification.Fingerprint acquisition can either be offline (inked) or Online (Live scan). In the inked method an imprint of an inked finger is first obtained on a paper, which is then scanned. This method usually produces images of very poor quality because of the nornunifbrm spread of ink and is therefore not exercised in online AFIS・ For online fingerprint image acquisition, capacitative or optical fingerprint scanners such as URU 4000, etc. are utilized which make use of techniques such as frustrated total internal reflection (FTIR)ultrasound total internal reflection1'^, sensing of differential capacitance141 and non contact 3D scanning⑴ for image development. Live scan scanners offer much greater image quality, usually a resolution of 512 dpi, which results in superior reliability during matching in comparison to inked fingerprints.Segmentation refers to the separation of fingerprint area (foreground) from the image background ⑹.Segmentation is useftil to avoid extraction of features in the noisy areas of fingerprints or the background・ A Simple thresholding techniquet ；l proves to be ineffective because of the streaked nature of the fingerprint area. The presence of noise in a fingerprint image requires more vigorous techniques for effective fingerprint segmentation. A good segmentation method should exhibit the following characteristics ⑻：• It should be insensitive to image contrast• It should detect smudged or noisy regions• Segmentation results should be independent of whether the input image is an enhanced image or a raw image• The segmentation results should be independent of image qualityRen et al.⑻ proposed an algorithm for segmentation that employs feature dots, which are then used to obtain a close segmentation curve. The authors claim that their method surpasses directional field and orientation based methods [9,I0J11 for fingerprint image segmentation. Shen et al.f,21 proposed a Gabor filter based method in which eight Gabor filters are convolved with each image block and the variance of the filter response is used both for fingerprint segmentation and quality specification. Xian et al.proposed a segmentation algorithm that exploits a block's cluster degree, mean and variance・ An optimal linear classifier is used for classification with morphological post-processing to remove classification errors・ Bazen et al.fl41 proposed a pixel wise technique for segmentation involving a linear combination of three feature vectors (i.e. gradient coherence, intensity mean and variance). A final morphological post-processing step is perfonned to eliminate holes in both the foreground and background. In spite of its high accuracy this algorithm has a very high computational complexity, which makes it impractical for real time processing. Klein et al.|l?1 proposed an algorithm that employs HMMs to remove the problem of fragmented segmentation encountered during the use of dif。